Unsupervised anomaly detection framework for multiple-connection based network intrusions

Unsupervised anomaly detection framework for multiple-connection based network intrusions

In this dissertation, we propose an effective and efficient online unsupervised anomaly detection framework. The framework consists of new anomalousness metrics, named IP Weight, and a new hybrid clustering algorithm, named I-means. IP Weight metrics provide measures of anomalousness of IP packet fl...

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Bibliographic Details
Main Author: Lu, Wei
Other Authors: Traoré, Issa
Language:English
en
Published: 2009
Subjects:
computer networks
security measures
computers
access control
UVic Subject Index::Sciences and Engineering::Applied Sciences::Computer science
Online Access:http://hdl.handle.net/1828/1949

Internet

http://hdl.handle.net/1828/1949

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